{"id":"W2632771481","doi":"10.1109/ccece.2017.7946711","title":"Evaluating trust models for improved event learning in VANETs","year":2017,"lang":"en","type":"article","venue":"","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Event (particle physics); Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0069287,0.001273894,0.001449467,0.0012013,0.0005014056,0.001991875,0.001560041,0.001296002,0.0009257284],"category_scores_gemma":[0.03050751,0.0005383907,0.0007690013,0.000754428,0.0007247266,0.003242625,0.001945781,0.001366742,0.0001862788],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002032666,"about_ca_system_score_gemma":0.001260588,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007298541,"about_ca_topic_score_gemma":0.004662334,"domain_scores_codex":[0.9967637,0.001623995,0.0003466567,0.0004503483,0.0005471059,0.000268304],"domain_scores_gemma":[0.9805644,0.01436452,0.001366579,0.001159808,0.002004786,0.0005398697],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003203466,0.00009653444,0.002633098,0.00004879565,0.00005582213,0.00003495972,0.00005388869,0.9704157,0.000368961,0.00204582,0.0002006803,0.02372544],"study_design_scores_gemma":[0.000004658766,0.00004071278,0.000104914,0.000003019986,0.000005091459,0.000006241298,0.00001092233,0.998746,0.0001889308,0.0008512193,0.00003582004,0.000002390409],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2945642,0.0009378577,0.700726,0.0005563044,0.0001206689,0.0001998396,0.0001965114,0.0008562998,0.001842451],"genre_scores_gemma":[0.9578175,0.0001420638,0.04134499,0.00003376673,0.00001886396,0.0000472894,0.0001847804,0.00002156731,0.000389077],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007298541,"threshold_uncertainty_score":0.03664291,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04003719152161394,"score_gpt":0.3126801818946124,"score_spread":0.2726429903729984,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}